Riemannian Sharpness Aware Minimization

Riemannian Sharpness-Aware Minimization (RSAM) aims to improve the generalization ability of deep learning models by optimizing within the geometric structure of the loss landscape, moving beyond traditional Euclidean space. Current research focuses on extending the Sharpness-Aware Minimization (SAM) algorithm to Riemannian manifolds, incorporating curvature information to better navigate complex loss surfaces, and applying these techniques to various architectures, including ResNets and Vision Transformers. This approach shows promise in enhancing model robustness and generalization, particularly for challenging tasks like image classification and remote sensing image segmentation, where improved performance with limited data is crucial.